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• W2735412187 abstract "This paper addresses the state constrained control problem of a class of nonlinear pure-feedback systems in the presence of unknown dynamics. Minimal learning parameter technique based neural networks are used to estimate the model uncertainties, thus the amount of online updated parameters is largely reduced. Filtered signals are introduced to avoid algebraic loop problems encountered in the implementation of the usual controllers. To overcome the problem of ‘explosion of the item’ and state constraints in the control design, barrier Lyapunov function (BLF) technique and sliding mode differentiator are incorporated into the neural back-stepping method and a new type of updated laws is presented. The highlight is that the proposed controller owns low-computation property and does not require mean value theorem to transform the system structure into affine strict-feedback form. The stability of the proposed control scheme is analyzed using the Lyapunov technique. Simulation results are obtained to illustrate the effectiveness of the proposed controller." @default.
• W2735412187 created "2017-07-21" @default.
• W2735412187 creator A5008813177 @default.
• W2735412187 creator A5085171003 @default.
• W2735412187 date "2017-05-01" @default.
• W2735412187 modified "2023-09-28" @default.
• W2735412187 title "State constrained adaptive neural control for a class of nonlinear pure-feedback system using barrier Lyapunov function" @default.
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• W2735412187 doi "https://doi.org/10.23919/acc.2017.7963519" @default.
• W2735412187 hasPublicationYear "2017" @default.
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